System for treating a subject with electrical stimulation based on a predicted or identified seizure

ABSTRACT

The present invention relates to a brain dysfunction and seizure detector monitor and system, and a method of detecting brain dysfunction and/or seizure of a subject. Preferably, the present invention also includes one or more seizure detection algorithms. The analysis method is specifically optimized to amplify abnormal brain activity and minimize normal background activity yielding a seizure index directly related to the current presence of ictal activity in the signal. Additionally, a seizure probability index based on historical values of the aforementioned seizure index, is derived for diagnostic purposes. The seizure probability index quantifies the probability that the patient has exhibited abnormal brain activity since the beginning of the recording. These indexes can be used in the context of emergency and/or clinical situations to assess the status and well-being of a patient&#39;s brain, or can be used to automatically administer treatment to stop the seizure before clinical signs appear.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.15/359,733, filed on Nov. 23, 2016 and which is a continuation of U.S.patent application Ser. No. 13/731,315, filed on Dec. 31, 2012 andissued as U.S. Pat. No. 9,538,950 on Jan. 10, 2017, and which is acontinuation of U.S. patent application Ser. No. 12/259,652, filed onOct. 28, 2008 and issued as U.S. Pat. No. 8,538,512 on Sep. 17, 2013,which is a continuation of U.S. patent application Ser. No. 12/148,815,filed on Apr. 23, 2008 and issued as U.S. Pat. No. 9,554,721 on Jan. 31,2017, which claims priority from U.S. provisional application No.60/925,785 filed Apr. 23, 2007.

LICENSE RIGHTS-FEDERAL SPONSORED

The U.S. Government has a paid-up license in this invention and theright in limited circumstances to require the patent owner to licenseothers on reasonable terms provided for by the terms of grant number 1U44 NS057969-01 awarded by the National Institutes of Health and grantnumber W81XWH-06-C-0016 awarded by Department of Defense USAMRAA.

BACKGROUND OF THE INVENTION 1. Field of the Invention

The present invention relates to a brain dysfunction and seizuredetector monitor and system, and a method of detecting brain dysfunctionand/or seizure of a subject. The present invention preferably is asystem capable of detecting in real time the presence of epilepticactivity based on recording of brainwave signal(s) such as scalpelectroencephalograms (EEG) or electro-corticograms (ECoG), see FIG. 1 .Both signals are used to assess brain function and are the best and mostused marker for seizure activity. This system incorporates astate-of-the-art signal processing algorithm based on time-frequencydecomposition of the signal, as disclosed herein. This system isintended to be used primarily in the following situations:

-   -   to help first responders such as Emergency Medical Technicians        (EMTs) to diagnose the presence of epileptic activity in        accident victims or patients for whom no medical information is        available,    -   to help nursing facilities, intensive care units, emergency        rooms, operating rooms etc., to monitor their patients and        provide timely treatment, by automatically detecting seizure        activity without the need for a trained neurologist or EEG        technologist who needs to continuously monitor and interpret EEG        recordings,    -   to help neurologists and EEG technologists review and mark long        term EEG and ECoG recordings for in depth determination of        seizure onset, type and location,    -   to provide early seizure detection for advanced therapeutics        such as Deep-Brain Stimulation and other timely treatment        delivery.

2. Technical Background

Detection of seizure activity in EEG and ECoG recordings has been theobject of intensive research in the past 50 years. Yet, most of theclinical work in this field still involves offline review of signaltracings by expert EEG technologists trained in the recognition ofpatterns in the signal indicative of seizure activity.

Seizures are usually classified into convulsive and non-convulsive.Convulsive seizures can be easily observed by attending medicalpersonnel since they involve involuntary muscle movements, convulsion,and twitching. Non-convulsive seizures, on the other hand, are moredifficult to diagnose accurately, since the patient is essentiallyun-responsive with no outward signs of seizure activity, which may bedue to a number of factors other than epilepsy. The consequence ofseizure activity can be dramatic. Any lasting un-controlled seizure canprovoke irremediable brain damage. Timely pharmacological interventionis necessary in order to stop the seizure and lessen the resultinginjury. Pharmacological intervention usually involves sedative drugswhich essentially suppress cortical activity in order to stop theseizure. This intervention is not without posing risk to the patient, asit also affects the circulatory system (bradychardia, hypotension,etc.). Thus the treatment should be closely supervised by trainedmedical professionals and it can be particularly risky in patients whoalready suffer from reduced cardiovascular reserves. It is alsoparticularly risky to administer the treatment to patients whose medicalhistory is not available. Having a means, such as the one disclosed inthe present invention, which accurately detects and diagnoses thepresence of ictal activity based on electro-physiological recordings canhelp medical professional in timely decision making to prescribe theadequate treatment. This can be particularly useful in emergencysituations, where trained neurologists and EEG technologists may not beavailable.

Most epileptic patients are aware of their condition and are providedwith a prophylactic treatment to control their seizures. They havealready been diagnosed and carry with them identification tags whichinform first responders of their medical situation in case of emergency.However, there are a number of situations for which a non-epilepticpatient may have seizures: traumatic brain injury resulting from a bluntforce trauma, concussion, or sudden acceleration/deceleration; poisoningfrom chemical agents, nerve gas, etc.; high fever. In these cases,seizures may be non-convulsive. First responders who are usually non-EEGexperts need to make an accurate determination of the patient state inorder to provide timely and adequate treatment. A device such as the oneproposed herein answers this need.

Another problem faced by medical teams in emergency situations isdrug-seeking individuals faking seizures in order to receivebenzodiazepine. These pseudo-seizures are a growing problem in the U.S.,where it is estimated that up to 40% of emergency admissions involvingpatients complaining of seizures are pseudo-seizures who then receive aninadequate treatment instead of the appropriate psychological andcounseling help.

In many clinical settings such as geriatric and palliative care,emergency rooms, operating rooms, intensive care units and otherhospital settings, patient's life signs are monitored continuously inorder to detect medical situations prompting immediate action fromattending medical professionals. The presence of seizure, essentiallynon-convulsive seizures, is often not detected nor diagnosed due to thecomplexity of having dedicated personnel reviewing streaming EEG or ECoGdata in real-time.

An exciting and promising potential treatment to arrest seizures is DeepBrain Stimulation (DBS), where implanted electrodes deliver anelectrical shock to the part(s) of the brain where the seizureoriginates. This electrical shock depletes neuro-transmitters locally,which, in turn, provides an effective barrier that prevents the ictalcascade to proceed beyond its originating point. The deep-brainstimulation treatment relies on the timely detection of the start of theseizure so that it can be applied before the manifestation of clinicalsigns. This treatment is applied only on a per need basis based on theearly detection of the seizure.

Currently available methods of automated EEG analysis for epilepticactivity detection have a number of significant disadvantages thatprevent their wider utilization in clinical applications, and inparticular, emergency and field applications. Such drawbacks include thefollowing: 1) Susceptibility to environmental noise/interference andbiological artifacts resulting in poor reliability; 2) Inability topreserve high signal quality resulting in poor reliability; 3) Lack ofmobility and portability preventing easy handling, transport andwearability; 4) Lack of robustness and inability to withstand roughhandling, water ingress and drop/vibration mechanical shocks; 5) Complexand time consuming application; 6) Complex interpretation of resultsrequiring EEG expert knowledge; 7) Insufficient accuracy; and 8) Delaysin detecting abnormal brain activity preventing timely diagnosis andintervention/prevention.

Better systems are needed for many types of applications such as masscasualty; battlefields; mobile hospitals; emergency intervention such asemergency rooms, on ambulances, on airplanes, on ships, and at accidentscenes; and locations within a hospital, such as intensive care unitsand operating rooms. An inexpensive, rugged, and field-deployable meansof automatically detecting the presence of EEG seizures and braindysfunction, followed by rapid and aggressive management, is essentialto ameliorate neuropathology from chemical and nerve agent exposure in amass casualty situation. Accurate detection of a non-convulsive seizureusing EEG analysis is of particular importance in treating victims ofnerve agent poisoning. A patient could be experiencing statusepilepticus (SE), yet due to depleted muscular stores of ATP, thepatient will not manifest convulsions. If electrical SE is present, thepatient should be given anticonvulsant. However, if no seizure isoccurring or the patient is post-ictal, more anticonvulsant couldcompromise patient respiration and should not be administered. Hence,being able to accurately detect the presence of EEG seizures in suchpatients is critical for correct treatment.

It is therefore an object of the present invention to provide a system,monitor and method that meets all of the above needs. It is anotherobject of the present invention that this method be inexpensive and/orrapid to conduct. It is still another object of the present inventionthat this method be usable by a person with no special medical training.It is still another object of the present invention that a patient'stherapeutic treatment be more accurately determined based on thequantitative number or profile derived from the testing of the patient.The object of the present invention also is to alleviate the abovelimitations by providing a rugged and reliable system for acquisitionand analysis of brainwaves obtained through intracranial or scalpelectrodes (EEG or ECoG signals). The system is compact, ruggedized,watertight and lightweight, preferably easy to attach to a stretcher, IVpole or patient garment such as a belt. It further comprises means formechanical shock and vibration protection. Such system also providesadvanced hardware for shielding the electronics from harmfulenvironmental noise and interferences, and advanced algorithms for thedetection and removal of various artifacts that commonly corruptneurophysiological signals. In addition, electronic means againstcardiac defibrillation therapeutic shocks enhances system usage inemergency situations. Continuous measurements of electrode-skin contactand monitoring of signal quality further enhance the reliability of theacquired brainwaves. All the above means ensure the reliability of thesystem, which is important for guaranteeing the adoption of the systemby non-EEG experts and medics, and thus its widespread use. Moreover,the system utilizes highly accurate algorithms for the timely detectionof epileptic and other abnormal brain activities. These algorithms andmethods are sensitive to such abnormalities. They are specificallydesigned such that they amplify abnormal activity, while minimizingnormal background activity.

SUMMARY OF THE INVENTION

The present invention relates to a brain dysfunction and seizuredetector monitor and system, and a method of detecting brain dysfunctionand/or seizure of a subject.

The accurate and real-time detection of abnormal brain activity providesthe means for the timely warning of impending seizure, thus enabling thetimely delivery of therapeutics to stop or abate seizures. Ultimately,our system provides real-time seizure and brain dysfunction detection,and can be used by either experts (e.g., EEG technologists,neurologists, etc.) and, more importantly, non-experts (e.g., firstresponders, non-medical volunteers). Its use can benefit many clinicalapplications, in particular emergency and intensive/critical caremedicine, neurology/neurosurgery and operating room applications,nursing home applications, field use in civilian and militaryapplications, victim triage, home use and monitoring, etc.

The present invention overcomes the drawbacks of the prior methods forautomated seizure detection. Its preferred embodiment enables the timelydetection of abnormal brain activity by utilizing ground-breakingadvances in signal processing, ergonomics, and electronics. The superioraccuracy of the detection is achieved through the use of a redundantwavelet transform, further enhanced by the subsequent synchronization ofthe wavelet coefficients in the different frequency bands ofdecomposition. This method tends to amplify abnormal activity andsuppress background “normal” activity. The subsequent integration of theamplified abnormal activity followed by the thresholding and resettingmechanisms yields the timely and accurate detection of brain dysfunctionpatterns such as seizures.

The various embodiments of the system of the present invention weredeveloped for the brain wave or activity monitoring of a single patientor multiple patients. Preferably, the system is a multi-channel EEGsystem, however, depending on purpose of use and cost, systems may haveas few as 1 channel. Preferably, the system or monitor of the presentinvention also includes one or more seizure detection algorithms. Theseseizure detection algorithms preferably will combine several parametricand non-parametric multi-EEG methods, as well as the instantaneous levelof consciousness and/or wake-sleep states. Preferably, the system ormonitor can also measure muscle activity, EMG, contained in the EEGsignal. In addition, the system and related methods can use othersensors that measure physiological signals which directly or indirectlyresult in or from brain dysfunction, or effect or result from brainactivity.

Preferably, the system or monitor is constructed to be rugged, so as towithstand transport, handling and use in emergency scenarios, such as onthe battlefield or in a mass casualty situation, or to reliably survivedaily use by emergency medical personnel or other first responders. Thesystem or monitor should preferably be splash-proof or water tight,dust-tight, scratch-resistant, and resistant to mechanical shock andvibration.

The system or monitor should preferably be portable and field-deployableto a military theater of operation, the scene of an accident, the homeof a patient, or to any clinical setting.

Preferably, the system or monitor should preferably be designed fornon-expert use. By this, it is meant that a person should not berequired to possess extraordinary or special medical training in orderto use the system effectively and reliably. Preferably, the systemshould therefore preferably be automatic in operation in a number ofrespects. First, the system should be capable of automatic calibration.Second, the system should preferably have automatic detection of inputsignal quality; for example, the system should be capable of detectingan imbalance in electrode impedance. Third, the system should preferablybe capable of artifact detection and removal, so as to isolate foranalysis that part of the signal which conveys meaningful informationregarding brain dysfunction and/or seizure. Fourth, the system shouldpreferably include seizure detection in the form of algorithms, theoutputs of which results in visual and/or audible feedback capable ofinforming the user whether a patient is currently seizing and/or theprobability of a patient having had a seizure at any time during theperiod of time that the system was monitoring the patient.

The system should preferably operate in real time. One example ofreal-time operation is the ability of the system to detect a seizure orbrain dysfunction event as it is happening, rather than being limited toanalysis that takes place minutes or hours afterward.

The processor or computer, and the methods of the present inventionpreferably contain software or embedded algorithms or steps that:automatically identify seizures or other brain dysfunction based on theamplified abnormal activity or ictal effects; automatically identifiesictal effects from the signals by comparing those effects with athreshold, automatically identifies seizures or other brain dysfunctionby first modifying the signal of a subject's brain wave activity toenhance the ictal activity and reduce the background activity related tonormal sleep and awake states of the subject; automatically identifiesbrain dysfunction or seizures by applying a wavelet algorithm toidentify ictal effects; automatically identifies seizures or braindysfunction by using redundant time-frequency decomposition followed bysynchronization of ictal effects; automatically identifies seizures orbrain dysfunction by combining two or more known or unknown methods ofbrain wave analysis to obtain a synergistic recognition method;automatically identifies seizures or brain dysfunction by integratingthe output of the wavelet analysis combined with synchronization andfurther applying appropriate thresholds and resetting mechanisms;combinations thereof; and the like.

Preferably, the system described in this invention also preferablyincorporates a number of unique features that improve safety,performance, durability, and reliability. The system should be cardiacdefibrillator proof, meaning that its electrical components are capableof withstanding the surge of electrical current associated with theapplication of a cardiac defibrillator shock to a patient beingmonitored by the system, and that the system remains operable aftersustaining such a surge. The system should have shielded leads so as toreduce as much as possible the effects of external electromagneticinterference on the collection of biopotential signals from the patientbeing monitored by the system. The system should be auto-calibrating,more preferably capable of compensating for the potential differences inthe gains of the input channels to the patient module. The system shouldbe capable of performing a continuous impedance check on its electrodeleads to ensure the suitability of monitored signals.

Preferably, the system should preferably be population-normed ratherthan individual-normed. That is to say, the system should be capable ofmonitoring a patient and making accurate determinations about thepatient's brain dysfunction of seizure status without first establishinga baseline measurement of normal brain activity to be used forcomparison against suspected dysfunctional brain activity or seizurecollected later.

Optionally, the system or monitor may be calibrated or tested via theutilization of a “virtual patient” device, which outputs pre-recordeddigital EEG in analog format and in real time in a manner similar towhat would be acquired from an actual patient, possibly with data frompatients with known brain dysfunction or brain wave abnormalities. Thisvirtual patient can also output any arbitrary waveforms at amplitudessimilar to those of EEG signals. These waveforms may be used for furthertesting of the amplification system, such as for the determination ofthe amplifier bandwidth, noise profile, linearity, common mode rejectionratio, or other input requirements.

The following are examples of different embodiments of the presentinvention. One embodiment of the present invention is a device fordetecting seizures or brain dysfunction comprising at least twoelectrodes each having a signal for detecting brain wave activity; and apatient module comprising at least one electronic component, the atleast one electronic component capable of inputting the signals from theat least two electrodes for detecting brain wave activity, amplifyingictal effects from the signals, automatically identifying seizures orother brain dysfunction based on the amplified ictal effects, andoutputting a calculated signal related to the identified seizures orother brain dysfunction.

Another embodiment of the present invention is a device for detectingseizures or brain dysfunction comprising at least two electrodes eachhaving a signal for detecting a subject's brain wave activity; and apatient module comprising at least one electronic component, the atleast one electronic component capable of inputting the signals from theat least two electrodes for detecting brain wave activity, automaticallyidentifying ictal effects from the signals by comparing with athreshold, the threshold being a predetermined number not based on atest subject's own brain wave activity, and outputting a calculatedsignal related to the identified seizures or other brain dysfunction.

Still another embodiment of the present invention is a device fordetecting seizures or brain dysfunction comprising at least twoelectrodes each having a signal for detecting a subject's brain waveactivity; and a patient module comprising at least one electroniccomponent, the at least one electronic component capable of inputtingthe signals from the at least two electrodes for detecting brain waveactivity, modifying the signal of the subject's brain wave activity toenhance the ictal activity and reduce the background activity related tonormal sleep and awake states of the subject; automatically identifyingictal effects from the modified signal, and outputting a calculatedsignal related to the identified seizures or other brain dysfunction.

Yet another embodiment of the present invention is a device fordetecting seizures or brain dysfunction comprising at least twoelectrodes each having a signal for detecting a subject's brain waveactivity; and a patient module comprising at least one electroniccomponent, the at least one electronic component capable of applying awavelet algorithm to automatically identifying ictal effects from thesignals, and outputting a calculated signal related to the identifiedseizures or other brain dysfunction.

Yet another embodiment of the present invention is a device fordetecting seizures or brain dysfunction comprising at least twoelectrodes each having a signal for detecting a subject's brain waveactivity; and a patient module comprising at least one electroniccomponent, the at least one electronic component capable automaticallyidentifying ictal effects from the signals, and outputting a calculatedsignal related to the identified seizures or other brain dysfunctionwherein the device is shielded for cardiac defibrillation or likevoltages.

Still yet another embodiment of the present invention is a device fordetecting seizures or brain dysfunction comprising at least twoelectrodes each having a signal for detecting a subject's brain waveactivity; and a patient module comprising at least one electroniccomponent, the at least one electronic component capable automaticallyidentifying ictal effects from the signals, performing a continuousimpedance check on the at least two electrodes and outputting acalculated signal related to the identified seizures or other braindysfunction.

Even yet another embodiment of the present invention is a device fordetecting seizures or brain dysfunction comprising at least twoelectrodes each having a signal for detecting a subject's brain waveactivity; and a patient module comprising at least one electroniccomponent, the at least one electronic component capable automaticcalibration of input channels, inputting the signals for detecting asubject's brain wave activity through the input channels, automaticallyidentifying ictal effects from the signals, and outputting a calculatedsignal related to the identified seizures or other brain dysfunction.

Yet another embodiment of the present invention is a device fordetecting seizures or brain dysfunction comprising at least twoelectrodes each having a signal for detecting a subject's brain waveactivity; and a patient module comprising at least one electroniccomponent, the at least one electronic component capable automaticallyidentifying ictal effects from the signals, and outputting a calculatedsignal related to the identified seizures or other brain dysfunctionwherein the device is potted and the at least one electronic componentis selected so as to eliminate microphonic effects so as to essentiallyeliminate any effects on the electrode or outputted signals.

Additional features and advantages of the invention will be set forth inthe detailed description which follows, and in part will be readilyapparent to those skilled in the art from that description or recognizedby practicing the invention as described herein, including the detaileddescription which follows, the claims, as well as the appended drawings.

It is to be understood that both the foregoing general description andthe following detailed description are merely exemplary of theinvention, and are intended to provide an overview or framework forunderstanding the nature and character of the invention as it isclaimed. The accompanying drawings are included to provide a furtherunderstanding of the invention, and are incorporated in and constitute apart of this specification. The drawings illustrate various embodimentsof the invention, and together with the description serve to explain theprinciples and operation of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 . Block diagram of a system overview for real-time applications.

FIG. 2 . Schematic of the real-time, automatic, field-deployable andruggedized brain dysfunction monitor.

FIG. 3 Schematic of a virtual patient bench test system.

FIG. 4 Flow diagram of the signal acquisition and pre-processing flowchart for one of the many embodiments.

FIG. 5 Flow chart and block diagram of redundant wavelet packet (RWP)decomposition (3-level).

FIG. 6 Examples of a RWP decomposition (2-level).

FIG. 7 Examples of synchronization 80 of the RWP coefficients for asingle spike.

FIG. 8 Example of the synchronization by shifting RWP coefficients inbands of decomposition using appropriate time shift.

FIG. 9 Example of the calculation of the spike detection coefficients byapplying a spike detection function on the shifted RWP coefficients.

FIG. 10 Examples of spike detection coefficients obtained for the singlespike example through the application of a spike detection function.

FIG. 11 Examples of an isolated spike in an ECoG recording and itsamplification by the spike detection function.

FIG. 12 Examples of two separate/different Spike Detection functions fordetection of a seizure onset in an ECoG recording.

FIG. 13 Block diagram depicting output filtering of the spike detectioncoefficients.

FIG. 14 Block diagram showing scaling of the seizure index.

FIG. 15 Example of the WAV_(SZ) index for a 5-minute scalp EEG recordingcontaining 2 separate generalized seizures.

FIG. 16 Block diagram of a seizure detection (Go/NoGo) based on WAV_(SZ)index and appropriate threshold H_(s).

FIG. 17 Block diagram of the calculation of the seizure probabilityindex for diagnostic purposes.

FIG. 18 Picture of the monitor screen for an embodiment of the presentinvention.

FIG. 19 Picture of the monitor screen for another embodiment of a userinterface for hand-held application.

FIG. 20 Picture of the monitor screen for still another embodiment of auser interface for hand-held application.

DETAILED DESCRIPTION OF THE INVENTION

In the preferred embodiment of the invention, a sensor apparatuscomprising, for example, at least two sensor electrodes placed incontact with the skin on the head of a patient, collects at least onechannel of brain activity such as an electroencephalogram (EEG) data andtransmits the EEG data to a patient module located on or near thepatient being monitored. Upon activation by an operator, the patientmodule initiates an auto-calibration, continuously checks for adequateelectrode/skin contact impedance, removes artifacts from the incomingEEG signal, and analyzes the incoming EEG signal using one or morealgorithms useful for detecting brain dysfunction or seizure. A visualdisplay on or connected to the patient module and/or one or more audiblealarms notify a nearby operator as to the neurophysiological status ofthe patient, in particular whether or not a patient is currently seizingand the probability of a patient having suffered a seizure at any timein the past while the patient was being monitored by the system. Inother embodiments of the invention, the patient module may transmit rawand/or analyzed data to remote locations for data storage, analysis,and/or display, and/or to notify a remote operator of a patient'spresent or recent neurophysiological status.

Various technologies may be used as part of the sensor suite orapparatus to collect brain activity or related data such as EEG datafrom the patient. Sensors that are used with various embodiments of thepresent invention are described herein but can also be any of thoseknown to those skilled in the art for the applications of thisinvention. These sensors include surface or intracranial electrodes formeasuring electrophysiological signals and brain related signals such asEEG, ECoG, EOG, EMG, and the like. These electrophysiological signals orbrain related signals can be obtained by any method known in the art, orby any method subsequently developed by those skilled in the art todetect these types of signals.

The sensors can also be magnetic sensors. Since electrophysiologicalsignals are, in general, electrical currents that produce associatedmagnetic fields, the present invention further anticipates methods ofsensing those magnetic fields to acquire brain wave signals similar tothose which can be obtained through, for example, an electrode appliedto the subject's scalp.

Presently-known and widely-used electrode technologies that may be usedby the invention to obtain brainwaves or other brain related-signalssuch as EEG signals are not limited to, but may include, for example,gold-plated cup electrodes that are filled with conductive paste,pre-gelled electrodes with adhesive backings and snap-on interfaces forthe electrode leads, or dry electrodes with penetrators that makecontact with the subcutaneous layer of skin.

Typical EEG electrodes connections may have an impedance in the range offrom 5 to 10 kiloohms. It is in generally desirable to reduce suchimpedance levels to below 2 kiloohms. Therefore a conductive paste orgel may be applied to the electrode to create a connection with animpedance below 2 kiloohms. Alternatively or in conjunction with theconductive gel, the subject(s)′ skin may be mechanically abraded, theelectrode may be amplified or a dry electrode may be used. Dryphysiological recording electrodes of the type described in U.S. Pat.No. 7,032,301 are herein incorporated by reference. Dry electrodesprovide the advantage that there is no gel to dry out, no skin to abradeor clean, and that the electrode can be applied in hairy areas such asthe scalp. Additionally if electrodes are used as the sensor(s),preferably at least two electrodes are used—one signal electrode and onereference electrode; and if further EEG or brain wave signal channelsare desired, the number of electrodes required will depend on whetherseparate reference electrodes or a single reference electrode is used.For the various embodiments of the present invention, preferably anelectrode is used and the placement of at least one of the electrodes ison the forehead of the subject's scalp.

The sensor apparatus or suite transmits brainwaves or other brainrelated signals such as EEG signals to the patient module. In thepreferred embodiment of the invention, analog EEG signals aretransmitted to the patient module by wires and are digitized andamplified by the patient module, such that the patient is tethered tothe patient module during monitoring. Other embodiments may useelectrodes or arrays of electrodes that individually amplify andtransmit brainwaves or other brain related signals such as EEG signalsto a patient module, either wirelessly or through wires, withdigitization of the signal taking place either within the electrodesprior to signal transmission or within the patient module followingtransmission.

In one embodiment of the invention, a harness of electrodes may be usedas the sensor apparatus to collect electrophysiological data from apatient. The harness provides the benefit of rapid application forimmediate primary monitoring plus optional additional electrodeapplication for more in-depth monitoring as time and situation allow.The electrode harness is composed of two separate parts: a frontalelectrodes array, which can be applied in seconds to the forehead of apatient, and a secondary flexible array that covers the entirety of theskull.

The frontal array of the electrode harness comprises a strip ofelectrodes disposed in line on sticky foam tape or similar adhesivesurface. Each electrode in the frontal array is connected to a printedcircuit that runs along the array and is terminated by a singleconnector. This connector is used to electrically connect the patientmodule to the electrode harness. This arrangement is used to acquire twofrontal EEG signals and provide a grounding point to the instrumentationamplifiers. Separate electrode connectors can also be used and ispreferable.

The secondary flexible array of the electrode harness, which covers theentire skull, may be added if more in-depth monitoring is warranted, orif time and situation allow. This array is preferably made out ofstretch material to fit many different head sizes. A single connectormay used to connect the flexible array to the frontal array through asecondary mating connector. Jaw straps may be used to maintain theflexible array in place and provide downward mechanical pressure on allelectrodes. Besides providing automatic electrode placement, theconstant pressure applied by the stretchable fabric of the array ensurescontinuous skin/electrode contact, even during patient movement ortransport.

In the preferred embodiment of the invention, the electrode leads thattransmit signals from the electrodes to the patient module are shieldedfor improved immunity from electromagnetic interference and resilienceto electrostatic discharge. Such electrode lead shielding is standard inelectrocardiogram (ECG) devices but is not normally used in EEG devices,as it reduces the input impedance of the amplifier, which then needs tobe compensated for by advanced design of the amplifier circuitry.Shielding used in the preferred embodiment of the invention provides thebenefit that an operator touching the leads will not create largeartifacts in the signal.

The preferred embodiment of the invention includes a patient modulecomprising electronics in a rugged enclosure, preferably with a visualdisplay and/or an audio speaker and/or a connection to an externaldisplay unit with video monitor and/or external speaker, and preferablywith one or more ports for connecting the sensor apparatus and/or otherequipment, including diagnostic test equipment. The patient modulepreferably includes a spring-loaded clamp mechanism to provide for itseasy mounting on an IV pole, a stretcher, or a hospital bed, and/or ahook hole so that it may be hung on a hook, as from an IV pole. Withinthe patient module, Brain waves or other brain related signals such asEEG signals are accepted from the sensor apparatus, filtered andprocessed to arrive at the brain dysfunction or seizure determinationreported to the operator via the display and/or audible alarms.

The patient module should preferably be portable, meaning that it shouldbe capable of being moved from one location to another while used orbetween periods of use while being carried by one or more persons, asdefined by International Electrotechnical Commission (IEC) standard60606-1, sub-clause 2.2.18, the entire standard which is herebyincorporated by reference. Preferably, the patient module weighs lessthan about 10 pounds. More preferably, the patient module weighs lessthan about 5 pounds. Even more preferably, the patient module weighsless than about 2 pounds. More preferably still, the patient moduleweighs less than about 1 pound. Preferably, the patient module is ofdimensions less than 7 inches by 4 inches by 2 inches.

The enclosure of the patient module can be constructed from most anyrigid material, including, but not limited to, various types of wood,various types of plastics, various types of polymers, various types ofresin, various types of ceramics, various types of metals, and varioustypes of composite materials. Preferably the box is constructed of anelectrically insulative and lightweight material such as a type ofplastic, rigid polymer, fiberglass, carbon fiber composite, or othermaterial with similar characteristics.

The patient module should preferably be of rugged construction, meeting,and preferably exceeding, standards consistent with the requirement forportable equipment to withstand the stresses caused by rough handlingand the dangers of ingress by dust or water. Characteristics of ruggedequipment include mechanical shock and vibration resistance, scratchresistance on functional and cosmetic surfaces, tightness to dust, andsplash proofing or better protection against the ingress of water(preferably water tight enclosure and connectors).

Preferably, the patient module should be resistant to mechanical shock.Mechanical shock resistance is defined by IEC 60601-1, clause 21, theentire reference which is hereby incorporated by reference. Preferably,the enclosure of the patient module should be rigid enough to withstandan inward-directed force of 45 newtons applied over an area of 625square millimeters anywhere on the surface. Preferably, the enclosure ofthe patient module should be capable of withstanding blows with animpact energy of 0.5 joules plus or minus 0.05 joules across an area ofat most 20 millimeters in diameter by a hammer with Rockwell hardnessR100 perpendicular to the surface of the enclosure at any point on theenclosure without breakage of the enclosure or damage to the componentsenclosed therein. Preferably, the enclosure of the patient module shouldbe capable of surviving a drop of 5 centimeters onto a 50 millimeterthick hardwood board without breakage of the enclosure or damage to thecomponents enclosed therein; more preferably, the patient module shouldbe capable of surviving a drop of 1 meter onto a 50 millimeter thickhardwood board without breakage of the enclosure of damage to thecomponents enclosed therein. Preferably, the carry handles or grips ofthe patient module should be capable of withstanding a force equal tofour times the weight of the patient module without breakage of thepatient module enclosure or damage to the components enclosed therein.

Microphonics, the noise introduced into an electronic system undergoingvibration resulting from the physical motion of the electricalcomponents, is detrimental to ambulatory signal acquisition andprocessing equipment. Signal distortion from microphonic effects maypreclude entirely the possibility of acquiring and analyzing usefulelectrophysiological signals while transporting a patient, for example,during a bumpy ride in a road vehicle such as an ambulance or on aflight in a vibrating aircraft such as a medical helicopter. Therefore,preferably, the patient module should be vibration-resistant, with theobjective of maintaining signal integrity while undergoing vibration byreducing microphonic effects. In the preferred embodiment of theinvention, shock absorbers are used for the reduction ofvibration-induced noise, and adequate potting and discriminate componentselection in the construction of the electronics further reducemicro-phonics. Preferably, the patient module should be capable ofwithstanding vibration at an acceleration spectral density of 0.05 g²per hertz over the frequency range of 10-500 hertz without breakage ofthe patient module enclosure or damage to the components enclosedtherein.

Preferably, the functional and cosmetic exterior surfaces of theenclosure of the patient module should be resistant to scratches.Scratches or abrasions associated with transport and handling of thepatient module should preferably not impair use of the patient module'scontrols or visual display.

Preferably, the patient module enclosure should be dust-protected,meeting the IEC 50529 IP5X standard for protection against ingress ofsolid foreign objects, the entire reference which is hereby incorporatedby reference. A dust-protected enclosure permits no accumulation of 75micrometer or smaller diameter particles to a degree or in a locationwhere such accumulation could interfere with the correct operation ofthe enclosed equipment or impair safety following the test prescribed bythe standard. More preferably, the patient module enclosure should bedust-proof, meeting the IEC 50529 IP6X standard for protection againstingress of solid foreign objects, which the reference which is herebyincorporated by reference. A dust-proof permits no observable deposit of75 micrometer or smaller diameter particles following the testprescribed by the standard.

Preferably, the patient module enclosure should be splash-proof, meetingthe IEC 50529 IPX4 standard for protection against ingress of water, thereference which is hereby incorporated by reference. An enclosure issplash-proof if water splashed against the enclosure from any directiondoes not interfere with the correct operation of the enclosed equipmentor impair safety. More preferably, the patient module enclosure shouldbe water-resistant, meeting the IEC 50529 IPX7 standard for protectionagainst ingress of water, the reference which is hereby incorporated byreference. An enclosure is water-resistant if ingress of water inquantities that could interfere with the correct operation of theenclosed equipment or impair safety does not occur when the enclosure isimmersed at a depth of 1 meter for 30 minutes. More preferably still,the patient module is waterproof and capable of sustaining indefiniteimmersion.

EEG equipment used in applications involving patients at risk ofcardiovascular failure, and patients under the influence ofpharmacological agents causing the depression of the autonomic nervoussystem, must be designed to be resistant to the shock of a cardiacdefibrillator. Therefore, preferably, the patient module of thisinvention should be cardiac defibrillator-proof, as defined by IEC60601-2-26, clause 17h, the entire standard which is hereby incorporatedby reference. Without cardiac defibrillator protection, electronic EEGdevices can be damaged by the application of a high-voltage cardiacdefibrillation shock to a patient to which EEG electrode leads areattached. Preferably, the patient module should be capable ofwithstanding an electric shock of 5 kilovolts applied directly betweenany 2 electrodes connected across a 100 ohm resistor load and return tonormal operation with 30 seconds after the application of thedefibrillation shock without loss of any operator settings or storeddata, without damage to the patient module or compromise to the safetyof the patient module, and without reducing the energy delivered by theshock to the resistor load by more than 10%. More preferably, thepatient module should able to withstand an electric shock of 6 kilovoltsapplied directly between any 2 electrodes connected across a 100 ohmresistor load and return to normal operation with 5 seconds after theapplication of the shock without loss of any operator settings or storeddata, without damage to the patient module or compromise to the safetyof the patient module, and without reducing the energy delivered by theshock to the resistor load by more than 10%. In the preferred embodimentof the invention, cardiac defibrillator proofing is achieved usingseries resistors in the cable that electrically connects the amplifierto the electrodes. This is standard in electrocardiogram (ECG) devices,but is difficult to implement for EEG devices since it can reduce thecommon mode rejection ratio and increase the noise profile. As a result,the series resistors must be handpicked to be perfectly balanced. Theymust also be of a particular technology to reduce the noise.

The patient module can be a separate unit with a signal processor or maybe comprised of a data acquisition unit and a base unit with the signalprocessor in the base unit. Preferably, the patient module comprises atleast one electronic component. Also preferably, the signals from one ormore of the aforementioned sensors are fed into the connectors on thepatient module through the sensor leads. The patient module preferablycomprises one or more electrical components which receive these signals,and then wirelessly transmit a signal to a monitor preferably on thepatient module. Preferably the patient module has a user interfacedevice to input information or to modify the parameters of the unit,however, in various embodiments this is not necessary.

One optional embodiment of this device is a programmable wireless dataacquisition system. This programmable wireless data acquisition systemis used to receive the signals from one of more sensors and work with aninternal or external processor to analyze the signals. Optionallyphysiological data may be transmitted between the physiological sensorsincluding electrodes of types described above and the signal processingmodule using wireless technology. Preferably, the wireless technology isradio frequency based. Most preferably the wireless technology isdigital radio frequency based. Preferably, the physiological data isprocessed to some extend directly in the patient module. Morepreferably, the physiological data is corrected for artifacts within thepatient module, and analyzed for. The physiological signals aretransmitted wirelessly to a receiver which can be a base station, atransceiver hooked to a computer, a PDA, a cellular phone, a wirelessnetwork, or the like. Most preferably the physiological signals aretransmitted wirelessly in digital format to a receiver which can be abase station, a transceiver hooked to a computer, a PDA, a cellularphone, a wireless network, or the like. Wireless signals are bothreceived and transmitted via use of an antenna, preferably external.Frequencies used for transmission are preferably less than about 2.0GHz. More preferably, frequencies used for transmission are primarily902-928 MHz, but Wireless Medical Telemetry Bands (WMTS), 608-614 MHz,1395-1400 MHz, or 1429-1432 MHz can also be used. The present inventionmay also use other less preferable frequencies above 2.0 GHz for datatransmission, including but not limited to such standards as Bluetooth,WiFi, IEEE 802.11, and the like. It is envisioned, but not as preferablethat the communication between the patient module and base station orlike device can also, or in addition to, be hardwired, instead ofwireless. The physiological sensors are generally hard wired to thesignal processing unit, but due to the ongoing evolution in wirelesssensor technology, signals from physiological sensors will likely betransmitted wirelessly to the signal processing unit, or alternatively,directly to the base station having an integrated signal processingunit, and as such are considered to be part of the present invention.With the advances in MEMS sensor technology the sensors may haveintegrated analog amplification, integrated A/D converters, andintegrated memory cells for calibration; allowing for some signalconditioning directly on the sensor before transmission.

Errors in the form of noise can occur when biopotential data acquisitionis performed on a subject. For example, a motion artifact is noise thatis introduced to a sensor signal that can result from motion of anelectrode that is placed on the skin of a subject. A motion artifact canalso be caused by bending of the electrical leads connected to anelectrode or even some other sensors. The presence of motion artifactscan result in misdiagnosis, prolong procedure duration and can lead todelayed or inappropriate treatment decisions. Thus, it is imperative toremove motion artifact from the biopotential signal to prevent theseproblems from occurring during treatment.

For methods of the present invention it is important to reduce motionartifacts from the sensor placement. The most common methods forreducing the effects of motion artifacts in sensors such as electrodeshave focused on skin deformation. These methods include removing theupper epidermal layer of the skin by abrasion, puncturing the skin inthe vicinity of the electrode, and measuring skin stretch at theelectrode site. The methods for skin abrasion ensure good electricalcontact between the electrode and the subject's skin. In this method, anabrasive pad is mechanically rotated on the skin to abrade the skinsurface before electrode placement. Moreover, medical electrodes havebeen used with an abrading member to prepare the skin after applicationof the electrode whereby an applicator gun rotates the abrading member.Methods of skin preparation that abrade the skin with a bundle of fibershave also been disclosed. The methods discussed above provide a lightabrasion of the skin to reduce the electrical potential and minimize theimpedance of the skin, thereby reducing motion artifacts. However, skinabrasion methods can cause unnecessary subject discomfort, prolongprocedure preparation time and can vary based on operator experience.Furthermore, skin abrasions methods can lead to infection, and do notprovide an effective solution to long term monitoring. Alternatively dryphysiogical recording electrodes could be used, instead of gelelectrodes. Dry physiological recording electrodes of the type describedin U.S. Pat. No. 7,032,301 are herein incorporated by reference. Dryphysiological electrodes do not require any of the skin abrasiontechniques mentioned above and are less likely to produce motionartifacts in general.

The above mentioned methods are indeed good practice to follow in thefield as they reduce motion artifacts, but they do not completelyeliminate motion artifacts. The invention possesses the ability to morecompletely remove motion and other artifacts by firmware and/or softwarecorrection that utilizes information collected preferably from a sensoror device to detect body motion, and more preferably from anaccelerometer. These and other artifacts can be denoised by methods suchas described in U.S. patent application Ser. No. 10/968,348 to Zikov etal, which is hereby incorporated by reference.

In certain embodiments of the present invention a 3-D accelerometer isdirectly connected to a microprocessing unit within the micromodulecontroller. The microprocessing unit receives signal inputs from theaccelerometer and a set of Brain waves or other brain related signalssuch as EEG signals. The microprocessor applies particular tests andalgorithms comparing the two signal sets to correct any motion artifactsthat have occurred. The processor in one embodiment applies a timesynchronization test, which compares the EEG signal data to theaccelerometer signal data synchronized in time to detect motionartifacts and then remove those artifacts. Alternatively the processormay apply a more complicated frequency analysis.

Frequency analysis preferably in the form of wavelet analysis can beapplied to the accelerometer and brainwaves or other brain relatedsignals such as EEG signals to yield artifact detection. Yet anotheralternative is to create a neural net model to improve artifactdetection and rejection. This allows for the system to be taught overtime to detect and correct motion artifacts that typically occur duringa test study. The above examples are only examples of possibleembodiments of the present invention not limitations. The accelerometerdata need not be analyzed before wireless transmission, it could betransmitted analyzed by a base station, computer or the like aftertransmission. As should be obvious to those skilled in the art that a2-D accelerometer or an appropriate array of accelerometers could alsobe used. Gyroscopes could be used as well for these purposes.

In addition, a video camera can be used to detect subject movement andposition, and the information then used to correct any artifacts thatmay have arisen from such movement. Preferably the camera is a digitalcamera. More preferably the camera is a wireless digital camera.Preferably, the video acquired from the camera is then processed so thatthe subject's movement and position are isolated from other informationin the video. The movement and position data that are acquired from thevideo is then preferably analyzed by software algorithms. This analysiswill yield the information needed to make artifact corrections of thephysiological signals.

One specific embodiment of the present invention using video subjectmovement detection involves the use of specially marked electrodes. Theelectrodes can be any appropriate electrode known in the art. The onlychange to the electrode is that they preferably have predetermined highcontrast marks on them to make them more visible to the video camera.These marking could be manufactured into the electrodes or simply be asticker that is placed on the back of the electrodes. These markingswill make it easier for the video system to distinguish the electrodesfrom the rest of the video image. Marking each visible electrode willallow for the calculation of the movement of each individual electrode;thus allowing for more accurate artifact correction.

Another specific embodiment of the invention does not require the use ofmarkings on the electrodes; instead the system can detect subjectmovement from monitoring the actual movement of the subject's body.Software is applied to the video that isolates the position of thesubject's body including limbs, then continues to monitor the motion ofthe subject.

There are numerous advantages to using video over other means ofartifact detection and correction. Foremost, video allows for thecalculation of movement artifacts from each individual electrode withoutthe need for accelerometers. This makes the use of video very costeffective in relation to other available methods. The video also can beused in conjunction with the accelerometer data to correct for motionartifacts, thus increasing the precision and accuracy of the system'smotion artifact correction capabilities.

Various embodiments of the present invention also allow for the flexibleuse of removable memory to either buffer signal data or store the datafor later transmission. Preferably, nonvolatile removable memory can beused as a way to customize the system's buffering capacity and forcomplete storage of the data. The micro-module controller for thepatient module can be programmed to send all signal data to theremovable memory or the micro-module controller can be programmed totransmit all data to the base station. If the micro-module controller isprogrammed to transmit the signal data to the base station wirelesslythe removable memory then acts as a buffer. If the patient module losesits wireless connection with the base station, remote communicationstation or the like, the patient module will temporarily store the datain the removable memory until the connection is restored and datatransmission can resume. If however the micro-module controller isprogrammed to send all signal data to the removable memory for storagethen the system does not transmit any information to the base station atthat time. That data stored on the removable memory can be retrieved byeither wireless transmission from the patient module to the base stationor like, or by removing the memory and directly reading. The method ofdirectly reading will depend on the format of the removable memory.Preferably the removable memory is easily removable, that can be removedinstantly or almost instantly without tools. In the current embodimentthe memory slot is located on the side of the patient module andprotected by a plastic cover. The memory is preferably in the form of acard and most preferably in the form of a small easily removable cardwith an imprint (or upper or lower surface) area of less than about 2sq. inches. If the removable memory is being used for data storage,preferably it should be of a type that can write data as fast as it isproduced by the system, and to possess enough memory capacity for theduration of the test. These demands will obviously depend on the type oftest being conducted, tests requiring more sensors, higher samplingrates and longer duration of testing will require faster write speedsand larger data capacity. The type of removable memory used can bealmost any type that meets the needs of the test being applied. Someexamples of the possible types of memory that could be used include butare not limited to Flash Memory such as CompactFlash, SmartMedia,Miniature Card, SD/MMC, Memory Stick or xD-Picture Card. Alternatively aportable hard drive, CD-RW burner, DVD-RW burner or other data storageperipheral could be to used. Preferably, a SD/MMC—flash memory card isused due to its small size. A PCMCIA card is not preferable because ofthe size and weight.

Preferably, the invention is capable of conducting a RF sweep to detectan occupied frequency or possible interference. The system is capable ofoperating in two different modes “manual” or “automatic.” In the manualmode the system conducts an RF sweep and displays the results of thescan on to the system monitor. The user of the system can then manuallychoose which frequency or channel to use for data transmission. Inautomatic mode the system conducts a RF sweep and automatically chooseswhich frequencies to utilize for data transmission. The system alsoemploys a form of frequency hopping to avoid interference and improvesecurity. The system scans the RF environment then picks a channel totransmit over based on the amount of interference occurring over thefrequency range.

Multiple brainwave activity or EEG data acquisition systems or patientmodules can be used or are capable of operating simultaneously in thesame geographical area without causing interference between theoperations of these systems. Preferably two or more subject's can behooked up to the brain wave activity or EEG data acquisition systems orpatient modules of the present invention and operate simultaneouslywithout interference and more preferably through one base station. Morepreferably at least four subject's can be hooked up to the brain waveactivity or EEG data acquisition systems or patient modules of thepresent invention and operate simultaneously without interference, evenmore preferably at least six subject's, even more preferably at leasteight subject's, still even more preferably at least sixteen subjects,even more preferably at least sixty four subjects, and most preferablyat least hundreds of subjects.

In various embodiments of the system of the present invention, thesystem or patient module also can include both a base station, andremote communication station or the like for receiving the transmittedsignal from the brainwave activity or EEG data acquisition systems orpatient modules. Optionally the base station or patient module may bepowered by a Universal Serial Bus (USB) from a computer or similardevice, if the base station is a computer or similar device it can beused to power the data acquisition system or patient module. The singleUSB connection provides the data connection to the computer and thepower from the computer to the base station. This allows for quick andsimple setup of the base station thereby improving the mobility of thesystem as a whole. The USB is also beneficial because an additional ACoutlet is not needed for the base station. This makes the system whenused with a portable laptop, properly equipped PDA or comparable devicecompletely wireless. The USB specification provides a single 5 volt wirefrom which connected USB devices may power themselves. The bus isspecified to deliver up to 500 mA. Because of these power limitations,the base station of the various embodiments of the present invention isunique. This is evidenced by the lack of wireless medical dataacquisition systems that employ a base station that is powered solely bya USB connection with the exception of the current invention. It is alsoevidenced by the much higher power requirements of the other basestations employed by other wireless medical data acquisition systems.Optionally, the patient module can also be powered by batteries.

If a wireless link is used in the system, preferably, the RF linkutilizes a two-way (bi-directional) data transmission. By using atwo-way data transmission, data safety is significantly increased. Bytransmitting redundant information in the data emitted by theelectrodes, the base station, remote communication station or the likeis capable of recognizing errors and request a renewed transmission ofthe data. In the presence of excessive transmission problems such as,for example transmission over excessively great distances, or due toobstacles absorbing the signals, the base station, remote communicationstation or the like is capable of controlling the data transmission, orto manipulate on its own the data. With control of data transmission itis also possible to control or re-set the parameters of the system,e.g., changing the transmission channel. This would be applicable forexample if the signal transmitted is superimposed by other sources ofinterference then by changing the channel the remote communicationstation could secure a flawless and interference free transmission.Another example would be if the signal transmitted is too weak, theremote communication station can transmit a command to increase itstransmitting power. Still another example would be the base station,remote communication station or the like to change the data format forthe transmission, e.g., in order to increase the redundant informationin the data flow. Increased redundancy allows transmission errors to bedetected and corrected more easily. In this way, safe data transmissionsare possible even with the poorest transmission qualities. Thistechnique opens in a simple way the possibility of reducing thetransmission power requirements. This also reduces the energyrequirements, thereby providing longer battery life. Another advantageof a two-way, bi-directional digital data transmission lays in thepossibility of transmitting test codes in order to filter out externalinterferences such as, for example, refraction or scatter from thetransmission current. In this way, it is possible to reconstruct falselytransmitted data.

The remote communication station can be any device known to receive RFtransmissions used by those skilled in the art to receive transmissionsof data. The base station, remote communication station or the like byway of example but not limitation can include a communications devicefor relaying the transmission, a communications device for re-processingthe transmission, a communications device for re-processing thetransmission then relaying it to another remote communication station, acomputer with wireless capabilities, a PDA with wireless capabilities, aprocessor, a processor with display capabilities, and combinations ofthese devices. Optionally, the base station, remote communicationstation or the like can further transmit data both to another deviceand/or back. Further optionally, two different remote communicationstations can be used, one for receiving transmitted data and another forsending data. For example, with the EEG wireless data acquisition systemof the present invention, the base station, remote communication systemor the like can be a wireless router, which establishes a broadbandInternet connection and transmits the physiological signal to a remoteInternet site for analysis, preferably by the subject's physician.Another example is where the base station, remote communication systemor the like is a PDA, computer or cell phone, which receives thephysiological data transmission, optionally re-processes theinformation, and re-transmits the information via cell towers, landphone lines or cable to a remote site for analysis. Another example iswhere the base station, remote communication system or the like is acomputer or processor, which receives the data transmission and displaysthe data or records it on some recording medium, which can be displayedremotely or on site for primary caretaker or clinician review anddiagnosis, or transferred for diagnosis or analysis at a later time.

The digitized kinetic or physiological signal is then transmittedwirelessly to a base station, remote communication station or the like.This base station, remote communication station or the like allows thesubject wide movement. Preferably, the base station, remotecommunication station or the like can pick up and transmit signals fromdistances of greater than about 5 feet from the subject, more preferablygreater than about 10 feet from the subject, even more preferablygreater than about 20 feet from the subject, still even more preferablygreater than about 50 feet from the subject, still even more preferablygreater than about 200 feet from the subject, and most preferablygreater than about 500 feet from the subject. The base station, remotecommunication station or the like can be used to re-transmit the signalbased in part from the physiological signal from the base station,remote communication station or the like wirelessly or via the Internetto another monitor, computer or processor system. This allows thephysician or monitoring service to review the subjects physiologicalsignals and if necessary to make a determination, which could includemodifying the patients treatment protocols.

Preferably, the patient module should be capable of detecting andfiltering high-frequency (HF) interference of the type associated withan electro-surgical unit (ESU). ESUs and other surgical equipment aretypical sources of high HF interference at frequencies greater than 100kHz and amplitudes several orders of magnitude higher than biopotentialslike EEG. When such interference creates a slight difference between thenoise at the recording electrode site and the noise at the referenceelectrode site, it may induce the saturation of an instrumentationamplifier collecting electrophysiological signals from a patient. Inthis case, the acquired signal cannot be salvaged, as it does notcontain any viable information. The patient module of the presentinvention should preferably contain special front-end circuitry toreduce and filter HF interference, for example of the type described inU.S. patent application Ser. No. 11/827,906, hereby incorporated byreference, or similar, thereby providing robustness of brain dysfunctionand seizure detection against HF interference. The preferred embodimentof the invention uses a modified Sallen-Key input filter with bootstrapfeedback to better guard against harsh interferences, such as thosecaused by ESUs and electrostatic discharges. The preferred embodiment ofthe invention also uses a 6 kV isolation interface, which provides avery low leakage capacitance to ground, which in turn furthers theability of the pre-amplifier to reject disturbances originating fromsuch interferences.

Preferably, the patient module has a low noise characteristic (e.g.,less than 1.5 micro volts peak-to-peak for the proper detection ofelectro-cortical silence—ECS) and wide bandwidth (e.g., preferably 0.125to 300 Hz to fit a wide population of patients from neonates to theelderly). In addition, preferably at least 1 channel is available, butmore preferably 8 channels (the more channels yield the more accurateresults) are available. Also, and preferably, automatic calibrationprocedure during startup is carried out to compensate for thedifferences in gains of the different channels. More preferably, thepatient module has the ability of measuring each electrode impedance atdifferent frequencies (in-band and out-band). This provides 2advantages: 1) the ability of continuously measuring each electrodeimpedance for guaranteeing an optimal signal quality; and 2) the abilityof detecting whether the electrode is of a good quality (e.g., agedelectrodes or poorly shelved electrodes or poor electrolytic gel orwrong electrodes can be detected).

Therefore, the patient module should incorporate the followingrefinements. First, the patient module should preferably includecalibration circuitry for testing the amplifier characteristic. Thecalibration is systematically carried out during the power-on startupsequence to verify the integrity of the system and its ability toproperly acquire Brain waves or other brain related signals such as EEGsignals. An automatic calibration during use is preferably provided aswell.

An impedance measurement circuitry is preferably added to measure theimpedance of each electrode during the initialization sequence. In caseof poor electrode impedance, warning messages advise the user to correctthe situation (e.g., by repositioning the faulty electrode(s), orre-preparing the electrode site(s)). In addition, preferably the abilityof continuously measuring the impedance is provided, without interferingwith the EEG frequency band. High electrode impedance is usually citedas the main reason for low signal quality. Having the ability ofmonitoring the impedance in real time without affecting Brain waves orother brain related signals such as EEG signals improves the robustnessof our system as it gives the operator a quantitative measure of signalquality.

Further, a “lead-off” detection circuit is added preferably in case oneof the leads gets disconnected. A warning LED on the face plate of thepatient module warns the user to check the electrode sensor. To furtheravoid cross-talk between the disconnected channel and the otheroperating channels, a relay shorts the inputs of the faulty channel,thereby minimizing its influence on other channels.

Finally, a tribo-electric shielded patient cable is added preferably,which contains cardiac defibrillator resistors and a proprietarycircuitry for improving the common mode rejection capability of thesystem. As compared to standard electrode leads, our cable provided animprovement of over 12 dB in common mode rejection. In addition, theleads have different lengths to easily connect onto a patient, and limitrisks for misconnection.

Laboratory test results have confirmed the excellent electricalcharacteristics of the patient module. In particular, its low noiseprofile, large bandwidth, excellent common mode rejection and inputimpedance, surpass the recommendations set by the InternationalFederation of Clinical Neurophysiology (IFCN) for digital EEGrecordings. This ensures superior signal quality even in the harshestenvironments. In addition, clinical tests have revealed that the patientmodule is immune to severe interferences.

Preferably, the patient module contains a processor or computer unit foranalyzing the brain wave and other signals from the electrodes andoptionally other signals from the subject. Optionally, the processor orcomputer unit may also be in a base station or at some remote site. Oneobjective of this invention is to provide a rapid, automated method ofbrain dysfunction or seizure detection usable by medical personnel notfamiliar with EEG signal acquisition, analysis, and interpretation.

The real time seizure detector is based on a continuous brainwavesignal(s) acquisition coupled with an advanced signal processing methodbased on redundant joint time-frequency transform such as stationarywavelet transform or, as in preferred embodiment, redundant waveletpacket transform. A preferred embodiment of such method is discussedbelow. For suboptimal results, other joint time-frequency transforms canbe used as well as other frequency domain transforms applied to ashort-duration epoch (e.g., short time Fourier transform).

The method preferably of automatic seizure and brain dysfunctiondetection can be comprised of one or more of 8 distinct steps, which aredetailed in the following for a 1-channel system. The steps are:

Step 1: Signal Acquisition and Pre-Processing

The system starts by continuously converting the analog brain waves orother brain related signals such as EEG signals into their digitalequivalents. The acquired digital EEG epoch contains a defined number ofsamples in sufficient quantity so that information pertinent to brainfunction and seizure activity is well represented. In the preferredembodiment, an EEG epoch is 1-second long and digitized at a rate of 128samples per second. Higher sampling rates can be envisaged in order toobtain a better representation of high frequency activity.

The digital epoch is additionally pre-processed. The pre-processing ofthe signal is done to remove the 50/60 Hz electromagnetic noise anddetect corrupting artifacts. Artifacts can be either physiological (EMGmuscle noise, ocular activity, EKG patterns, sweat artifact, etc.) orenvironmental (noise from lead movement and vibrations, etc.). Epochsheavily corrupted by certain artifacts may not be of sufficient qualityto detect seizures. These epochs are thus discarded from the analysis,or, more preferably, de-noised in order to extract the valid EEG or ECoGinformation embedded in the signal. In the preferred embodiment,wavelet-based de-li) noising technique, such as described in Zikov etal., U.S. patent application Ser. No. 10/968,348, which is herebyincorporated by reference.

In the preferred embodiment, additional real-time pre-processingfunctions are used to determine the patient's state of consciousness,the level of muscle activity, the level of ocular activity, the presenceof electro-cortical silence (ECS—indicative of potential brain trauma,coma, death, and/or profound pharmacological effect). This informationcan be used as a complement to or for refinement of the seizure detectordetailed below. This information may also be displayed to the user, inaddition to the detected seizure activity information, or may be used totrigger specific alarms related to brain status of the patient. Thisinformation can also be obtained after the determination of the presenceof seizure activity.

The signal quality is first determined by assessing the electrodeimpedance and measuring the 50/60 Hz content in the original signal. Ifthe signal quality is adequate, the algorithm then proceeds by analyzingthe signal for the presence of corrupting artifacts. Whenever possible,these artifacts should be removed from the signal (de-noising function).Artifact-free epochs or de-noised epochs are then analyzed to extractsecondary parameters. It then proceeds with the seizure detectionoutlined in the next Steps.

Step 2: Stationary Wavelet Transform (SWT) and Redundant Wavelet Packet(RWP) Decomposition

In the preferred embodiment, a series of digital filters are applied tothe analyzed epoch of brain activity. Using a redundant decompositionsuch as the SWT or RWP allows an enhanced time resolution in comparisonto standard (i.e., non-redundant) Wavelet Transform and Wavelet Packetdecompositions. The use of redundant transform results in improvedaccuracy of the epileptic activity detection.

In redundant decompositions, the number of coefficients obtained in eachset of coefficients in different frequency bands of decomposition isequal to the number of samples in the analyzed signal epoch. Thedecomposition is achieved through a filter bank. The filter coefficientsof the filter bank at the first level of decomposition are up-sampled ateach subsequent level of decomposition in order to obtain theinformation at that level of decomposition. Conversely to the standardtransform, the output of the filter is not down-sampled at each level.

In the preferred embodiment, we use a N=2-level RWP decomposition, wherethe signal S is decomposed into a 2^(N)=4 sets of coefficientscorresponding to 4 frequency bands:S→{C _(i)}_(i∈[1:2) _(N) _(]) ,{C _(i) }={q ₁ ,q ₂ , . . . ,q _(j) , . .. q _(M)}_(i)  (1)

where the coefficients of the set C₁ represent the approximationcoefficients, the coefficients of the sets C₂ to C₄ contain the N-leveldetail coefficients, and where M is the number of samples in each epoch.The decomposition can be performed using any wavelet filter. However, inthe preferred embodiment, we use a Daubechies #8 filter, as it captureswell the characteristics of typical epileptic patterns such as epilepticspikes. Note that, due to the use of the redundant transform combinedwith the later synchronization step, it is not necessary to use awavelet basis function of a low order to achieve good detection ofabnormal activity. In fact, the improved recognition is achieved if ahigher order wavelet filter is used due to a better frequency separationof the various brain patterns (e.g., artifacts vs. epileptic activity).

Step 3: Synchronization of the RWP Coefficients

Due to the nature of the FIR filters used in the decomposition filterbanks, time synchrony is of essence. For instance, if a spike isdecomposed, such as shown in FIG. 7 later in the application, thecoefficients can be observed in the different sub-bands do not peak atthe same point in time.

An important principle of the seizure detection method is to detectepileptic spike activity embedded in the signal. In order to improve thedetection capability of the algorithm, it is useful to synchronize thewavelet coefficients such that they peak at the same time in eachfrequency band of decomposition. This is achieved by generating the newcoefficient sets {Cs_(i)}defined as:{Cs _(i) }={q ₁ ^(s) ,q ₂ ^(s) , . . . ,q _(j) ^(s) , . . . ,q _(M)^(s)}_(i) ={q _(1-T) ,q _(2-T) _(i) , . . . ,q _(j-T) _(i) , . . . ,q_(M-T) _(i) }_(i),  (2)

where T_(i) is a time shift for the decomposition band i. The time shiftdepends uniquely on the filter used for the decomposition. It is usuallydifferent for each decomposition bands, and can be either positive ornegative. Note that whenever j−T_(i)<0, coefficients obtained from theprevious epoch can be used, whereas whenever j−T_(i)>M, the mirrorcoefficient M−j+T_(i) can be used instead.

Step 4: Spike Detection

The next step consists of obtaining a set of detection coefficientsD(j)=f(Cs_(i,j)), where the spike detection function f is a linear ornon-linear function optimized for spike detection in Brain waves orother brain related signals such as EEG signals. For example:

$\begin{matrix}{{D(j)} = {{f\left( {Cs_{i,j}} \right)} = {\prod\limits_{i = 1}^{2^{N}}{{❘C_{i,{j - T_{i}}}❘}.}}}} & (3)\end{matrix}$

The function (3) works remarkably well on ECoG signals and signalspoorly perturbed by EMG artifacts. For scalp EEG signals, it is oftengood practice to minimize the weight of the RWP coefficients in higherfrequency bands. In the case of N=2-level redundant wavelet packetdecomposition based on a signal sampled at 128 S/s, (3) becomes:

$\begin{matrix}{{{D(j)} = {{f\left( {Cs}_{i,j} \right)} = {\prod\limits_{i = 1}^{2^{N} - 1}{❘C_{i,{j - T_{i}}}❘}}}},} & (4)\end{matrix}$

where the coefficients in the 32-64 Hz band were removed from the spikedetection function. Likewise, in signals that may be easily perturbed byocular activity and sweat artifacts, it may be judicious to limit theinfluence of the low frequency band RWP coefficients. A generalizationof (3) is:

$\begin{matrix}{{{D(j)} = {{f\left( {Cs}_{i,j} \right)} = {\prod\limits_{i = 1}^{2^{N}}{❘\left( C_{i,{j - T_{i}}} \right)❘}^{\gamma_{i}}}}},{\gamma_{i} \in \Re},} & (5)\end{matrix}$

where the γ_(i) exponents act as weighting factors. Through variousoptimization procedures, it is possible to define optimal γ_(i)exponents for different EEG and ECoG montages.

Another spike detection function can be based on the sum of the RWPcoefficients instead:

$\begin{matrix}{{{D(j)} = {{f\left( {Cs}_{i,j} \right)} = {\sum\limits_{i = 1}^{2^{N}}{\alpha_{i} \cdot {❘C_{i,{j - T_{i}}}❘}^{\gamma_{i}}}}}},{\alpha_{i} \in \Re},{\gamma_{i} \in \Re}} & (6)\end{matrix}$

where α, and γ_(i) are weighting factors. Based on a 2-level Daubechies#8 redundant wavelet packet decomposition, we found that the followingfunction works well on scalp EEG data potentially corrupted by EMGartifacts and ocular activity (e.g., fronto-temporal montages):D(j)=|C ₁(j+6)|±C ₂(j−3)² +|C ₃(j−5)|+|C ₄(j+2)|  (7)

It is further possible for one skilled in the art to define numerouslinear or non-linear functions based on this step.

Step 5: Output Filtering

The output of the spike detector is a time series of coefficients withlow values during normal brain activity, and very large values duringabnormal brain activity such as epileptic spikes. Since seizures arecharacterized by the rapid succession of such spikes, these large valuestend to occur frequently during seizures.

To better characterize periods of seizure and other abnormal brainactivity, it may be advantageous to filter the output of the spikedetector, which can be done in many different ways.

In one embodiment, the D(j) coefficients are integrated over time. Theoutput of the integrator is reset to 0 whenever the input has been lessthan a pre-determined threshold H for a pre-determined length of time L.The threshold H can be determined, for example, by analyzing the outputof the detector on non-seizure data obtained from healthy subjects orepileptic patients. This data is preferably collected on healthyindividuals. The same threshold can be used for each electrode montage,or, preferably, be tuned for each individual channel and differentmontages. The latency period L should typically be larger than the timebetween two consecutive spikes during seizures. L can be determined, forexample, based on seizure data collected from a population of patients,or based on the current patient data. Similarly to the threshold H, thelatency period can be determined for each individual channel anddifferent montages separately.

In the preferred embodiment, the D(j) coefficients are first compared tothe threshold H. Whenever D(j)≤H, the coefficient D(j) is set to 0. Thiseffectively removes the background noise associated with normal EEG orECoG activity from the spike detector coefficients. The de-noisedcoefficients are then integrated and reset in a similar way as describedabove. For example, using the spike detector function (6), the noisethreshold H can be set to 100 for scalp EEG fronto-temporal montages,and the latency L is about 1 second. It may also be advantageous tolimit the amplitude of the spike detector coefficients so that any onecoefficient cannot overpower the output of the filter. A secondarythreshold H′ is then used. Any coefficient D(j) higher than H′ is set tothe H′ value. The secondary threshold may be required for spikedetection functions such as (4), which typically yield high values ofcoefficients. The filtered output of this step is further referred to asD_(o)(j).

Step 6: Scaling

The output of the output filter in Step #5 is essentially an index ofseizure activity. This index can be expressed in an easy to understandscale in order to facilitate its interpretation. In the preferredembodiment, the 0 to 100 scale is used, where a value of 0 isrepresentative of normal brain activity, and a value of 100 isrepresentative of seizure activity. Intermediate values are typicallyobtained during, for example, the start of seizure activity or isolatedspike activity. The resulting index is clamped to 100. Other scales canbe envisaged. The resulting scaled seizure index will be referred in thefollowing as the wavelet-based seizure index (WAV_(SZ)). The WAV_(SZ)index is computed for every EEG epoch.

An example of the WAV_(SZ) index is presented in FIG. 15 shown later inthe application for a 5-minute scalp EEG recording containing 2 separateseizures. It is interesting to note how the seizure index resets to 0immediately upon seizure discontinuation.

Step 7: Go/NoGo Determination

In the preferred embodiment, a seizure threshold H_(S) between 0 and 100is applied to the WAV_(SZ) in order to help clinicians and firstresponders make a Go/NoGo determination related to the presence ofseizures. This determination can be done as follow:

$\begin{matrix}{{G(k)} = \left\{ \begin{matrix}{1,{{{iif}\ {{WAV}_{sz}(k)}} > H_{S}}} \\{0,{{{iif}{{WAV}_{sz}(k)}} \leq H_{S}}}\end{matrix} \right.} & (8)\end{matrix}$

A unique seizure threshold can be applied for all electrode montages.More preferably, the seizure threshold is determined for a specificelectrode montage and channels. Montages that are more sensitive toartifacts and muscle movement may have a higher threshold. Montages thatare less sensitive to artifact, as well as ECoG recordings may havelower thresholds.

It is important to note that the Go/NoGo seizure detection determinationcan be made more or less conservative depending on the choice for theseizure threshold. A conservative choice may be judicious in field-likesituations, where the presence of corrupting artifacts is typicallyhigher. This effectively reduces the incidence of false positives.Likewise, a low seizure threshold value may be a better choice incontrolled clinical environments, or when the patient is unconscious(less EMG and ocular artifacts). In this case, even subtle seizures maybe detected by the system.

In the preferred embodiment, the seizure threshold is automaticallyselected by the system based on statistics involving presence ofdetected artifacts, signal quality, muscle activity, the patient'sconsciousness state, or combination thereof. In another embodiment, theseizure threshold is defaulted to an average value. In yet anotherembodiment, the threshold can be selected by the user of the system.

It is important to note that the Go/NoGo determination has numerousreal-time applications, such as automated alarm/warning (visual andauditory), and automated therapeutics administration.

Step 8: Seizure Probability Index

The WAV_(SZ) derived in Step #6, as well as the associated Go/NoGodetermination, are representative of the current state of the patient.It is important to note that both the index and the seizuredetermination reset to their nominal state indicative of normal brainactivity as soon as the seizure disappears. In order to provide thesystem with diagnostic capability, it is then advantageous to add amechanism through which the patient state can be tracked, i.e., throughwhich the presence of seizure activity in the recent past is accountedfor to provide first responders and clinicians with an automateddiagnostic tool.

This can be done by computing the probability P_(SZ) that the patienthas experienced seizure activity in the recent past. In the preferredembodiment, P_(SZ) is computed as follow:

$\begin{matrix}{{P_{SZ}(k)} = \left\{ {\begin{matrix}{{P_{SZ}\left( {k - 1} \right)} + \frac{\left( {{{WAV}_{SZ}(k)} \cdot {G(k)}} \right)}{100} - \lambda} \\{0,{{{{iif}\ {P_{SZ}\left( {k - 1} \right)}} + \frac{\left( {{{WAV}_{SZ}(k)} \cdot {G(k)}} \right)}{100} - \lambda} < 0}}\end{matrix},} \right.} & (9)\end{matrix}$

It is important to note that when λ=0, the P_(SZ) value increases onlywhen the seizure threshold has been crossed by the WAV_(SZ). While thisis rare in the absence of seizures, this may happen when series ofnon-detected artifacts perturb the recorded signals. These falsepositives trigger the Go/NoGo determination, which also increasesP_(SZ). In order to limit the influence of these false positives, it istherefore important to add a means to reduce P_(SZ) slowly over time.This is achieved through the use of the forgetting factor λ. In thepreferred embodiment, where the analysis is performed on a per secondbasis and the background normal activity is removed from the spikedetector coefficients, we can set λ=0.001. It is important to note thatthis value depends on the sensitivity of the Go/NoGo determination.

The P_(SZ) value is then interpreted in order to provide an appropriatediagnosis. In the preferred embodiment, a simple method consists ofhaving 3 LEDs (green, amber, red). A low P_(SZ) value (between 0 and0.5) lights only the green LED. An average value (between 0.5 and 20)lights the amber LED. A high value (above 20) lights the red LED. Thered LED indicates that the patient most probably suffers seizures. Thegreen LED essentially signifies that the patient had no seizure sincethe system was attached to the patient and started. The amber LEDindicates that further review of the data may be necessary to make anaccurate diagnosis.

In another embodiment, the system is provided with a screen and userinterface. In this case, text messages and/or pictograms representingthe seizure probability index are displayed on the screen.

For real-time operation, Steps #1 thru #8 are repeated often enough toprovide a rapid assessment of the patient's state. In the preferredembodiment, a refreshing rate of 1 second is used. Faster refreshingrates can be envisaged. In order to use all of the data, it ispreferable that the epoch size be no shorter than the refreshing rate ofthe algorithm. Having a longer epoch length results in an analysisoverlap, which may not be computationally efficient and may bedetrimental to real-time implementation.

For this application real time is defined as being less than about 10minutes, preferably less than about 1 minute, more preferably less thanabout 20 seconds, even more preferably less than about 5 seconds, stillmore preferably less than about 1 second, and more preferably less thanabout 0.1 seconds. Real time can also be based on the minimum unit oftime between two samples corresponding to the sampling rate not to beless than 50 Hz.

The previous method was described for operation based on a 1-channelsystem. Typically, however, multi-channel systems are used in order toobserve focal activity, detect focal brain abnormalities, and/or improvethe sensitivity and specificity of the analysis.

For a multi-channel system, Steps #1 to #6 are carried through for eachindividual channel. Additional analyses that are based on theavailability of multiple channels (e.g., cross-correlation methods) maybe added in Step #1 for a more accurate determination of the patient'sbrain state. It is also possible to consider that some of the analysesin Step #1 be performed only for a selected subset of channels, thussaving valuable processing resources. For example, consciousnessdetermination and EMG quantification can be done solely on frontalchannels. Electro-cortical Silence (ECS) can also be determined based on1 single channel, as long as the electrode pairs are sufficientlyspaced.

Once Steps #1 thru #6 are repeated for each channel, a number ofWAV_(SZ) indexes are obtained. These indexes are referred in thefollowing as WAV_(SZ) ^(c), where the superscript c denotes the channelnumber. The following steps are then applied:

Step #7′: Go/NoGo Determination

In a multi-channel system, the Go/NoGo determination is done byconsidering each individual WAV_(SZ) ^(c), index. In this case, weexpect to see a rise in the WAV_(SZ) ^(c), indexes only for the channelswhere focal seizures are present. A Go/NoGo determination for both focaland generalized seizures is then based on whether the seizure thresholdhas been crossed by some of the WAV_(SZ) ^(c) indexes. In situationsinvolving a limited number of channels, the crossing of the threshold inonly one channel may be sufficient to trigger a seizure flag. In thiscase:

$\begin{matrix}{{G(k)} = \left\{ \begin{matrix}{1,{{{iif}\ \max\left\{ {{{WAV}_{SZ}^{c}(k)} - H_{s}^{c}} \right\}_{c}} > 0}} \\{0,{{{iif}\max\left\{ {{{WAV}_{SZ}^{c}(k)} - H_{s}^{c}} \right\}_{c}} \leq 0}}\end{matrix} \right.} & (10)\end{matrix}$

where H_(s) ^(c) are channel dependent seizure thresholds. This methodgives the highest sensitivity but also the lowest specificity.

As the number of channels increases, such as with a full 10-20 electrodemontage, or with a cortical grid, it may be advantageous to trigger theseizure detection only when a pre-defined number of WAV_(SZ) ^(c)indexes cross their respective seizure thresholds. This effectivelyreduces the number of false positives, and increases the specificity ofthe detector. In yet another embodiment, cluster analysis may be used.In this case, the seizure detection is triggered when a group of closelylocated electrode pairs acquire signals that contain seizure activityoriginating from the same source.

Step #8′: Seizure Probability Index

The seizure probability index is defined similarly to the 1-channelcase, with the difference that the product of the maximum of theWAV_(SZ) ^(c), indexes with the Go/NoGo determination of Step #7′ isused instead. In the preferred embodiment, where the analysis is done ona per second basis, and where the WAV_(SZ) ^(c), indexes are scaledbetween 0 and 100, the seizure probability index is defined as:

$\begin{matrix}{{P_{SZ}(k)} = {\left\{ \begin{matrix}{{P_{SZ}\left( {k - 1} \right)} + \frac{\max{\left\{ {{WAV}_{SZ}(k)} \right\}_{c} \cdot {G(k)}}}{100} - \lambda} \\{0,{{{{iff}\ {P_{SZ}\left( {k - 1} \right)}} + \frac{\max{\left\{ {{WAV}_{SZ}(k)} \right\}_{c} \cdot {G(k)}}}{100} - \lambda} < 0}}\end{matrix} \right..}} & (11)\end{matrix}$

The brain dysfunction or seizure detection and diagnosis method, whichis the object of the present invention, can easily be programmed forreal-time applications. Yet, in one embodiment, the method can also beused to analyze pre-recorded data, in order to provide clinicians withan automated review and diagnostic mechanism. In this embodiment, thesystem is composed of a mass storage sub-system which contains the datato be analyzed. The computing means accesses and reads these data, andprovides them to the automated seizure detector and diagnosis algorithm.The detector calculates the WAV_(SZ) ^(c) indexes and P_(SZ) value basedon the provided data. In this embodiment, the computing means downloadsthe next data epochs as soon as the analysis for the previous epoch hasended. This essentially increases the speed at which the analysis iscarried out, and takes full advantage of the processor speed of thecomputing means.

DETAILED DESCRIPTION OF THE DRAWINGS

Now referring to the FIGS. 1-20 , FIG. 1 is a block diagram of a systemoverview for real-time applications. The system can be connected to thesubject either on the subject's scalp 1 a with mounted surfaceelectrodes 1, intra-cranial cortical grids 2, or implanted deep brainelectrode(s) 3. The electrode leads 1 b are preferably connected to thesystem via a yoke 4 containing cardiac defibrillation resistors (notshown) designed to absorb the energy of a cardiac defibrillation pulse.These resistors (not shown) and the associated electronics in thefront-end of the instrumentation amplifiers (not shown) are designed toprotect the instrumentation electronic while ensuring that most of theenergy delivered by the pulse is used for the intended therapy. Thebrainwave signals are then amplified and digitized by an Analog-DigitalConverter (ADC) circuitry 5.

In addition, a Signal Quality (SQ) circuitry 6, 7 can be used to injectmeasurement currents into the leads in order to calibrate theinstrumentation amplifiers and measure the electrode impedance. Asimilar SQ circuitry monitors the front-end amplifiers in order todetect eventual saturation that occurs when leads 1 b are disconnected.This information, along with the digitized brainwave signals, is relayedto the processing means 8-14.

The processing means is composed of the sub-systems 8 thru 14. TheSignal Quality Assessment Module 8 is used to check whether each signalacquired by the system is of sufficient enough quality to be used in thesubsequent analysis. This is done by measuring continuously theelectrode impedance of each brainwave channel, and by quantifying thelevels of 50 and 60 Hz noise in the signal. High levels of 50 or 60 Hzindicate either a poor electro-magnetic environment, or a poorconnection to the patient which will result in a heightened sensitivityof the system for any other environmental noise (e.g., lead movement,vibration, etc.). High levels of 50 or 60 Hz noise are usuallyindicative of poor signal quality.

If the signal quality is good, the system proceeds by analyzing theacquired signals in order to detect the presence of environmental orphysiological artifacts, which may be corrupting the signal. Someartifacts, such as ocular artifacts, can be removed from the signal byusing a de-noising method. This is done at the level of the ArtifactDetection & Removal Module 9.

De-noised and artifact-free signals are sent to the BrainwaveAnalysis/Processing Module 10. This sub-system derives informationcontained in the signal, such as the level of consciousness of thepatient, the presence of electro-cortical silence, the level of ocularactivity, the level of muscle activity (EMG), etc. This information canbe used as a complement to the real-time seizure detector to provide abetter diagnostic means to the user. Some of this information may alsobe used in the real-time seizure detector to tune properly the differentthresholds used by the underlying algorithm.

The Automated Detection & Decision Module 11 is at the core of thereal-time seizure detector. It uses a method that amplifies abnormalspike activity in the signal, while minimizing the background “normal”brain activity. It also combines the real-time seizure index with theinformation obtained in the Brainwave Analysis/Processing Module 10 inorder to provide an accurate diagnostic of the patient's brain state.

A User Interface Module 12 provides the means for the user to interactwith the system. In the preferred embodiment, this is done through theuse of a display 13, which can be a touch screen display. The display 13is used to warn the user, in real-time, of the presence of seizures. Inaddition, the User Interface Module 12 archives all the acquired signalsand processed variables into a mass storage device 14 for later review.

Finally, in some embodiments, the system is connected to mechanism thatautomatically delivers a treatment to the patient, referred in theschematic as the Treatment Delivery Device 15. The output of the systemthrough a processor (not shown) can be used with the Treatment DeliveryDevice in closed loop 16 to automatically deliver physical, electricalor chemical treatment to the subject automatically based on theoccurrence of abnormal brain activity, and monitor the effectiveness ofsuch treatment in real time.

FIG. 2 shows a schematic of the real-time, automatic, field-deployableand ruggedized brain dysfunction monitor. The Patient Monitor 20presents itself under the form of a plastic enclosure 25 housing theelectronics (not shown) and the computing and display means 23. Theenclosure 25 is designed in such a way that water and dust ingress isminimized. The system has 2 connectors 21, 22. The Power/Data connector21 provides the means to supply power to the system, recharge thebattery, and transfer data. The Patient Cable connector 22 provides themeans of attaching a cable containing the electrode leads. This cablecan be removed for easy cleaning. In the preferred embodiment, a touchscreen display 23 is embedded in the enclosure, and provides users withan interface to display the patient's brain state, and interface withthe instrumentation amplifiers.

The enclosure has also the means of securely fastening the system to anintravenous pole and/or stretcher bars. This is done using a locking orspring-loaded clamp at the back of the unit. Another attachment means isprovided in the form of an opening 24 in the plastic enclosure 25. Thisopening is used to hang the system onto a hook.

FIG. 3 shows a virtual patient bench test system. A bench test systemwas designed in order to provide the means for testing the real-timecapability of the system. The bench test system makes use of a databaseof pre-recorded EEG signals contained in a mass storage sub-system 30,which can be accessed wirelessly (not shown), through a network (notshown), or through a serial/parallel data link 38. The pre-recordedsignals are accessed by a computer 31, such as a laptop. The data can bedisplayed on the laptop screen 32 for visual inspection. They arefurther formatted and sent through a serial/parallel data link to thesub-system 33, further referred to as Virtual Patient (VP). The VP 33buffers a short segment of the digital EEG data. Once the buffer isfull, the VP 33 outputs the digital data to a Digital-to-AnalogConverter (DAC) 34, which converts the digital data into an analogequivalent. The digital data are sent to the DAC 34 at a rate consistentwith real-time. Once sent to the DAC 34, they are removed from thebuffer. Communication means between the computer and the VP ensures thatthe buffer is always kept full by adding new data from the originaldatabase. This communication may be done in real-time or faster.

An attenuator stage 35 reduces the amplitude of the analogue signal 36in such a way that a micro-volt range is achieved. The gain of theattenuator if specifically designed such that the VP output is anequivalent, both in terms of waveform and amplitude, to the originaldigital data. Care is taken to reduce the output noise of the VP suchthat the ECS detection methods and algorithms can be tested (the outputnoise of the VP superimpose to the analogue EEG equivalent, which mayprevent the monitoring system to detect periods of ECS).

Besides for pre-recorded EEG data, the bench test VP can also be used togenerate arbitrary waveforms to evaluate the electrical characteristicsof the instrumentation amplifiers of the tested system.

FIG. 4 shows a diagram of the signal acquisition and pre-processing flowfor one of the many embodiments. After the acquisition of the currentEEG epoch 42, the signal quality is measured by means of continuousimpedance 44 and Electro-Magnetic (EM) noise 46 measures. If the signalquality is good 48, the system proceeds by applying a series ofpre-processing filters 50. Resampling is carried out at this stage inorder to provide the different algorithms with signals that are of theproper rate. Preferably, a sampling rate of 128 S/s is used for mostanalyses and signal processing methods. A sampling rate of 256 S/s isused to determine the EMG power. Once pre-processing is done, thealgorithm proceeds to determining whether corrupting artifacts arepresent 52 in the signal. In some cases, corrupting artifacts can beremoved from the signal by denoising 54 without affecting the underlyingtrue EEG information needed for the subsequent analyses. In this case,the algorithm proceeds by calculating secondary variables 56 which mayoffer a complement of information regarding the patient's brain state.In case the corrupting artifacts are too severe to be removed (e.g.,they corrupt the whole frequency band), the epoch is dropped out of theanalysis.

In the preferred embodiment, the secondary variables are theelectromyogram (EMG) activity (power in the 70-110 Hz band), aconsciousness index representing the level of consciousness of thepatient, and the detection of electro-cortical silence (ECS) in order todetermine the suppression ratio (percentage of ECS epoch in the last 60seconds).

FIG. 5 is a flow chart and block diagram of redundant wavelet packet(RWP) decomposition (3-level). In FIG. 5 , the redundant signaldecomposition 60 is carried out through three levels of filter banks 62.

FIG. 6 are examples of a RWP decomposition (2-level). The originalsignal S 70 contains random white noise as well as different frequencycomponents, and a single spike 72 at time t=102 samples. It isinteresting to see how the redundant decomposition captured well thesecomponents in the signal. Even the short transitory spike is evident 74in the high frequency band 78.

FIG. 7 are examples of synchronization 80 of the RWP coefficients for asingle spike 82.

FIG. 8 is an example of the synchronization by shifting RWP coefficients90 in bands 92 of decomposition using appropriate time shifts 94.

FIG. 9 is an example of the calculation of the spike detectioncoefficients 100 by applying a spike detection function 102 on theshifted RWP coefficients 104.

FIG. 10 are examples of spike detection coefficients 110 obtained forthe single spike example (not shown) through the application of a spikedetection function 112.

FIG. 11 are examples of an isolated spike 120 in an ECoG recording 122and its amplification by the spike detection function 126. Theamplification effect of the synchronization of the wavelet coefficientsis obvious, as it yields more prominent detector coefficients 124 whichcan be more easily distinguished from normal background EEG activity.

FIG. 12 are examples of two separate/different Spike Detection functions130, 132 for detection of a seizure onset 134 in an ECoG recording 136.The start of the seizure 134 is very well detected 138 even though theinitial spikes are buried in the background activity. The first spikedetection function yields more prominent coefficients and is better atminimizing 140 the artifact 139 localized at the beginning of therecording. Yet, it is more sensitive to EMG activity and the secondfunction 132 is preferred for scalp EEG analysis.

FIG. 13 is a block diagram depicting output filtering 150 of the spikedetection coefficients. Thresholding 152 is coupled with a resettableintegrator 154 to yield an un-scaled seizure index 156. The switch 158in the integrator loop 160 is controlled through a reset circuitry 162.When the output 164 of the reset circuitry 162 is 1, the switch 158toggles to its high position 166 and outputs 0. The reset circuitry 162effectively counts the number of thresholded D(j) coefficients 168 thatare equal to 0 in the last L samples. If this number is equal to L, thecircuitry 162 outputs 1. It outputs 0 otherwise. The output of thefilter is a single value which represents the seizure activity of thekth epoch.

FIG. 14 is a block diagram showing scaling 170 of the seizure index. Inthe preferred embodiment, the WAV_(SZ) scale 171 is obtained through alimiter 172 with a scale from 0 to 100, where 0 denotes the absence ofany seizure activity, and 100 denotes the presence of strong andsustained seizure activity. In the preferred embodiment, the K factor174 is 1e⁻⁹.

FIG. 15 is an example of the WAV_(SZ) index 190 for a 5-minute scalp EEGrecording 192 containing 2 separate generalized seizures 194, 196. Therecording started in the middle of a seizure. The WAV_(SZ) index 190 canbe shown to automatically detect the beginnings and the ends of theseizures where it crosses above a threshold H_(s) 204, represented onthe graph by a dashed white line.

FIG. 16 is a block diagram of a seizure detection (Go/NoGo) 200 based onWAV_(SZ) index 202 and appropriate threshold H_(s) 204.

FIG. 17 is a block diagram of the calculation of the seizure probabilityindex 210 for diagnostic purposes.

FIG. 18 shows the monitor screen 230 for an embodiment of the presentinvention under two different conditions. Screen 220 shows a userinterface for a_hand-held application. This interface displays thereal-time WAV_(SZ) index Lwhich is zero and therefore not shown), asuppression ratio (also zero and therefore not shown), and the EMGactivity over a defined amount of time 226. During normal activity, thebackground 228 of the trends remains green (indicated in the figure bystippling). When seizure activity is detected as shown in similar screen224, the background shading turns red 232 (indicated in the figure byabsence of stippling underneath the seizure index curve), therebyclearly indicating periods of detected seizure activity in the past. Inthe upper right hand side, text messages 234 provide an interpretationof the different processed variables that are displayed to the user.User controls are provided in the lower right hand side corner 236. Notethat the interface in the preferred embodiment is designed for a smalltouch screen.

FIG. 19 is a picture of the monitor screen 240 for an embodiment of auser interface for hand-held application. This interface displays thereal-time EEG waveforms 242 for easy visual inspection. The diagnostictext messages 244 are kept in the upper right hand-side corner. Channelswith poor electrode impedance or disconnected leads are automaticallyremoved 246 from the subsequent analyses. While no analysis is beingperformed, the system keeps on continuously monitoring these channels inorder to detect an eventual improvement in the signal quality. At thispoint, the channels are automatically enabled are re-incorporated in theanalysis scheme. It is important to notice that the analysis methods areautomatically adapted depending on the availability of EEG channels.

FIG. 20 illustrates monitor screens 250 for an embodiment of a userinterface for hand-held application. These status pages can be used todisplay various information that may be useful to more advanced users.In particular, the interface can display in real-time all, or a sub-setof, the processed variables 252, including, per cannel, seizure,consciousness, suppression ratio, and EMG variables. This can includevariables calculated to determine the patient's brain state, as well asvariables calculated to determine signal quality 254. In addition,identification information pertaining to the patient 256 and theattending user 258 can be added and displayed. Finally, informationrelated to the system itself (calibration values, serial number,software version, remaining battery life and disk space, etc.) 260 canalso be displayed. The information presented in the Status Page can spanmany different pages which can be accessed through Forward/Backwardbuttons 262.

It will be apparent to those skilled in the art that variousmodifications and variations can be made to the present inventionwithout departing from the spirit and scope of the invention. Thus, itis intended that the present invention cover the modifications andvariations of this invention provided they come within the scope of theappended claims and their equivalents.

What we claim is:
 1. A system for detecting and treating seizures withelectrical treatment comprising: at least two brain electrodesconfigured for implantation into the brain, the brain electrodes adaptedto detect brain wave activity and provide signals of the brain waveactivity detected; a patient module adapted for a patient comprising aprocessor configured with an algorithm, (1) wherein the processor isadapted to receive the signals from the at least two electrodes, and (2)wherein the algorithm, when executed by the processor, causes theprocessor to: (a) apply a transform to the signals to obtain at leasttwo coefficients from the signals, (b) align the coefficientscorresponding with at least one abnormal pattern signal, (c) combine thealigned coefficients, (d) output an output signal or data correspondingto the aligned and combined coefficients to an output device, and (e)identify a seizure of the patient based at least in part on comparingthe aligned and combined coefficients corresponding to the at least oneabnormal pattern signal to a threshold; and the output device adapted totreat seizure through the at least two brain electrodes by administeringan electrical treatment to the patient based at least in part on thealigned and combined coefficients corresponding with the at least oneabnormal pattern signal to treat the seizure.
 2. The system of claim 1,wherein the output signal is output to the output device for treatmentpurposes in real time.
 3. The system of claim 2, wherein the patientmodule is adapted to be positioned external to a body of the patient. 4.The system of claim 3, further comprising a harness adapted toincorporate at least two leads wherein the leads or the harnesscomprises shielding adapted to protect the patient module fromelectromagnetic interference and electrostatic discharge.
 5. The systemof claim 1, wherein the processor and algorithm are further adapted tointegrate the aligned and combined coefficients corresponding with theat least one abnormal pattern signal over time to create an index, theindex is represented as a numerical value corresponding to the level ofabnormal pattern signal, and where the index is compared against thethreshold to determine whether seizure activity is occurring in thepatient in real-time.
 6. The system of claim 1, wherein the patientmodule is adapted to transmit raw and/or analyzed data to a computer ora cellular phone for display, and/or to notify a care provider of apresent or recent neurophysiological status of the patient.
 7. Thesystem of claim 1, wherein the patient module is adapted to transmit rawand/or analyzed data to remote locations for data storage, analysis,and/or display, and/or to notify a remote operator of a present orrecent neurophysiological status of the patient.
 8. A system fordetecting and treating seizures with electrical treatment comprising: atleast two electrodes adapted to detect brain wave activity and providesignals of the brain wave activity detected; a patient module adaptedfor a patient comprising a processor configured with an algorithm, (1)wherein the processor is adapted to receive the signals from the atleast two electrodes, and (2) wherein the algorithm, when executed bythe processor, causes the processor to: (a) apply a transform to thesignals to obtain at least two coefficients from the signals, (b) alignthe coefficients corresponding with at least one abnormal patternsignal, (c) combine the aligned coefficients, (d) output an outputsignal or data corresponding to the aligned and combined coefficients toan output device, and (e) identify a seizure of the patient based atleast in part on comparing the aligned and combined coefficientscorresponding to the at least one abnormal pattern signal to athreshold; and the output device adapted to treat seizure byadministering an electrical treatment to the patient based at least inpart on the aligned and combined coefficients corresponding with the atleast one abnormal pattern signal to treat the seizure.
 9. The system ofclaim 8, wherein the output signal is output to the output device fortreatment purposes in real time.
 10. The system of claim 9, wherein thepatient module is adapted to be positioned external to a body of thepatient.
 11. The system of claim 10, further comprising a harnessadapted to incorporate at least two leads wherein the leads or theharness comprises shielding adapted to protect the patient module fromelectromagnetic interference and electrostatic discharge.
 12. The systemof claim 8, wherein the patient module further comprises at least oneelectronic component adapted to perform a continuous impedance check onthe at least two electrodes while continuing to detect brain waveactivity with the at least two electrodes.
 13. The system in claim 8,wherein the patient module is adapted to transmit raw and/or analyzeddata to a computer or a cellular phone for display, and/or to notify acare provider of a present or recent neurophysiological status of thepatient.